Vehicle systems may include monitors that perform various on-board diagnostic routines to check the health of the vehicle system. As an example, an emissions monitor may be mandated to periodically evaluate the functionalities of relevant systems, such as by diagnosing various sensors of the vehicle's engine system, diagnosing fuel system leak checks, assessing engine emissions triggers, etc. As such, each diagnostic routine performed by a monitor may have specific entry and/or execution conditions. These conditions may, in turn, be dependent on a plurality of variable parameters such as the vehicle or engine's operating conditions, energy storage conditions, customer usage of the vehicle, etc. In other words, the evaluations performed by the monitors may be trustworthy only when specified driving conditions and/or environmental conditions (the “entry and execution conditions”) are met. However, due to the variability in vehicle conditions, the trigger and complete execution of a monitor's routines may not be guaranteed. For example, a routine may be initiated but aborted before completion due to execution conditions not being met. Alternatively, initiation of a routine may be delayed due to entry conditions not being met.
Various telematics based approaches have been developed to facilitate emissions compliance. For example, as shown by Fiechter et al. in U.S. Pat. No. 6,609,051, the use of machine learning and data mining technologies on data acquired from many vehicles is used for diagnostic applications. Therein, sensor data and information from on-board diagnostic systems are collected and monitored at an off-board site with data mining and data fusion algorithms applied for data evaluation. The data is also used to predict the state of a component.
However, the inventors herein have recognized that even with such approaches, a vehicle may be deemed non-compliant. For example, in addition to completion of the various diagnostic routines, emissions compliance of a vehicle may require the collection of high level statistics of the routines (e.g., the number of triggers, the number of full executions of a routine, the number of full executions that are flagged as pass, etc.). Regulatory agencies may conduct random sampling of the statistics and assess significant penalties if the results are not satisfactory. For example, penalties may be assessed if a monitor does not attempt a routine often enough, if the routine is aborted too often, if the routine is not flagged as pass often enough, etc. Thus, the approach of Fiechter may not sufficiently address at least the denominator component of accumulated monitor execution statistics that is subjected to government inspection.
In one example, some of the above issues may be at least partly addressed by a method for a vehicle having an engine comprising: initiating one or more on-board engine diagnostic routines based on predicted engine operating conditions, the prediction based on an operator's driving pattern. In particular, entry conditions for the one of more on-board engine diagnostic routines may be adjusted (e.g., temporarily relaxed) based on the predicted engine operating conditions. In this way, minimum monitor execution requirements may be met while also improving full execution of in-vehicle monitors.
As an example, frequent drive cycles of a vehicle operator may be evaluated with respect to the entry and execution conditions of one or more on-board diagnostic routines. In addition, habitual information may be gained by recursively learning driving patterns specific to the vehicle operator by using various in-vehicle sensors. Based on the data collected from the vehicle operator's driving patterns, future patterns of vehicle operation and expected engine operating conditions may be predicted. On-board diagnostic routines may then be initiated based on the predicted engine operating conditions. In particular, instead of triggering the execution of a diagnostic routine based on current engine operating conditions, the preview of future patterns may be assessed to determine if it may influence the trigger or inhibition of the routine. Thus, if the predicted operating conditions meet the entry and full execution conditions for a particular diagnostic routine, the given diagnostic routine may be initiated and completed more reliably. On the other hand, if the current conditions meet the entry requirements for a diagnostic routine but the predicted operating conditions indicate that full execution of the routine may not be possible, the vehicle controller may evaluate the risk associated with early abortion of the routine. If the penalty associated with early abortion of the routine is higher, the controller may temporarily prohibit entry of the diagnostic routine. In other examples, such as where there is a high risk or penalty associated with a routine being executed too infrequently, the entry and/or execution conditions of the routine may be adjusted, for example, temporarily relaxed. Relaxing the conditions may include making the requirements less stringent, such as, for example, by lowering the threshold for at least one parameter associated with the entry and execution conditions of the diagnostic routine. This may be achieved, for example, by increasing a vehicle speed range in which the diagnostic is enabled to run (or decreasing a vehicle speed range in which the diagnostic is not enabled to run).
In this way, statistical and stochastic models may be used to encapsulate a vehicle operator's driving pattern. Vehicle operating conditions may then be predicted based on the learned driving pattern. By adjusting the entry and execution of an on-board diagnostic routine based on the entry and execution conditions of the routine relative to the predicted vehicle operating conditions, the initiation and completion of diagnostic routines may be better enabled without reducing the credibility of the produced results. Likewise, by selectively relaxing the entry and execution conditions of a routine based on the predicted vehicle operating conditions, diagnostic routine completion numbers can be improved. Overall, accumulated monitor execution statistics can be improved by increasing both the denominator and the numerator. In addition, vehicle emissions compliance is better enabled.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.